Automating Metadata Management for Long-Term Success

In my previous two blog posts, I discussed critical aspects of metadata management, including how to define and organize metadata and how organizations can leverage data governance to build a successful metadata management strategy. In this final blog in my metadata management series, I’ll cover how organizations can automate key metadata management processes.

Metadata management provides data users with necessary information about their data assets, including its location, when and how it was created, its meaning, usages and more. However, the specifics regarding data’s lineage, integrity level, relationships to other data assets, etc. are always subject to change.

As with any data management process, whether metadata management, monitoring data quality or tracking lineage, automation is critical to sustainable success. For metadata management, automating asset curation can minimize manual metadata tasks, ensuring that metadata stays accurate and trustworthy across the data supply chain.

How do Businesses Automate Metadata Management?

Manual metadata management tasks, such as data cataloging, data tagging, identifying relationships between data sets and linking related business terms, can drain a significant amount of IT’s time and energy. However, organizations can now take advantage of metadata to help automate some manual efforts.

Gartner uses the term ‘active metadata’ to describe the ways in which metadata can be used to trigger or activate processes and automate operations. Active metadata is part of an advanced data management system in which metadata is used to automate some data management tasks and optimize operational and analytical processes.

In this way, metadata is active in shaping and reshaping the data management environment. Machine learning models, can leverage active metadata to automate complex processes enterprise-wide.

Benefiting from Active Metadata and Automation

Active metadata helps automate a wide variety of processes, including:

• Managing Data Quality Risk: Active metadata can significantly reduce an organization’s resources to manage data. By applying data quality rules, active metadata can prompt the capture of missing, outdated, or invalid metadata. Active metadata can be used to identify comparable data sets and administer the same data quality rules instead of manually creating new rules for each data set. 
• Building Recommendation Engines: Recommendation engines enable easy-to-use data asset discovery for all data users. For example, when a user searches for a particular type of data, active metadata finds similar data sets that may be of interest to the user. This functionality enables and expands analytical insights. 
• Providing Additional Context to Data: When a data user looks for data to analyze, active metadata directs them to the exact information they need. Not only does active metadata enable data discovery, it automatically supplies additional context on how applicable data sets are for a given purpose.
• Eliminating Wasteful Information: Active metadata tells businesses their data usage, what data is antiquated and what data is redundant. All of this information is crucial to eliminating unused, duplicate or outdated data within data repositories.

Active metadata equips businesses with critical information they need to organize and define metadata, and to develop the proper strategies that allow successful metadata management. Still, they need the right tools, technologies and processes to create active metadata and enable automation.

Selecting Modern Technologies that Facilitate Metadata Automation

Today’s technologies have integrated capabilities for data governance, data quality and analytics to take metadata management to the next level. By selecting a data intelligence platform that features all three capabilities, as well as pre-built and customizable connectors, organizations can quickly and easily gather many types of metadata from multiple sources and automatically supply active metadata that ensures a complete view of an organization’s data landscape.

Data quality capabilities should administer checks for completeness, conformance, validity and integrity. Active metadata ensures that data is handled correctly according to business rules and that the data remains accurate and reliable as it travels through multiple systems. Analytics capabilities also apply machine learning algorithms for constant self-learning to enhance data quality.

The data intelligence platform should also have automatic data discovery capabilities, allowing for the continual capture and monitoring of changes to metadata. Transformation in metadata is then automatically discovered and applied across the data supply chain to deliver essential data insights.

Automating metadata management processes is a significant business advantage. When more processes are automated, users spend less time searching for and wrangling data. This time savings enables them to shift their focus to analysis and the development of meaningful business intelligence.

Are you looking for additional information about automating metadata management processes? Check out the Gartner Magic Quadrant for Metadata Management Solutions 2019 below.